Jul 2026· The Journal of Theoretical Accounting Research· 0 citations· 28 references
Abstract
This study examines the role of Artificial Intelligence (AI) in enhancing supply chain project management and operational performance in a dynamic business environment. As supply chains become increasingly complex, data-intensive, and disruption-prone, organizations are adopting AI-driven tools to improve forecasting accuracy, optimize inventory, streamline logistics, and strengthen decision-making. The purpose of this research is to assess the level of AI adoption, identify key application areas, and examine the relationship between AI familiarity and AI adoption while considering the broader roles of organizational readiness and governance mechanisms. A quantitative research design was employed using a structured questionnaire administered to 42 respondents, including supply chain professionals, project managers, data/AI analysts, students, and other business or technology-related participants. Data were analyzed using descriptive statistics, correlation analysis, and regression techniques. The findings indicate that approximately 57% of respondents reported current AI adoption within their organizations, while the mean AI familiarity score was 3.6 on a five-point scale, reflecting moderate awareness. Correlation analysis revealed a positive relationship between AI familiarity and AI adoption (r = 0.61), suggesting that increased knowledge supports adoption behavior. The results also highlight the perceived importance of AI training, organizational preparedness, and governance frameworks in maximizing implementation benefits. This study contributes to business analytics, operations management, and decision sciences by providing empirical insight into AI-enabled supply chain transformation. The findings offer practical implications for managers, policymakers, and industry stakeholders seeking to strengthen AI readiness, improve operational efficiency, and promote responsible AI adoption for sustainable supply chain excellence.
The study concludes that AI adoption serves as a strategic organizational capability that significantly enhances strategic planning effectiveness and suggests that organizations leveraging AI technologies are more likely to develop effective strategies, improve decision quality, enhance forecasting accuracy, and strengthen organizational adaptability.
Mark Ian C. Abrias, Nerissa M. Revilla· World Journal of Advanced Re...· 0 citations
The article examines the potential of artificial intelligence to optimize management processes and improve decision-making in large organizations. The study is conducted within the framework of a business engineering approach. The analysis is based on data from the Stanford AI Index 2026 database. It evaluates the level of implementation of artificial intelligence–based decision support systems across different industries and business functions. The results reveal statistically significant differences in AI adoption between industries and functional areas (Friedman criterion, p < 0.05). The highest level of integration is observed in the technology sector, particularly in IT, software development, marketing, and sales. In contrast, manufacturing and supply chain/inventory management remain the least digitized areas. The study proposes a conceptual AI-based management framework. The framework integrates data sources, analytical models, and decision support systems into a unified adaptive cycle with a feedback mechanism. The findings confirm that the effectiveness of artificial intelligence depends not only on the technological sophistication of the solutions. It also depends on the depth of their integration into management processes and on the development of effective human–AI collaboration models. Finally, the article provides practical recommendations for scaling AI-based solutions in large organizations.
Serhii Kubitskyi, Yevhen Kozlovskyi, K. Balabukha et al.· Human Resources Management a...· 0 citations
The study concludes that AI-driven analytics significantly enhances organizational performance through improved predictive analytics, decision automation, and data-driven strategic planning to maximize organizational benefits from AI technologies.
O. Enyinnaya, O. Onwuegbule, K. M. Amasiatu et al.· British journal of managemen...· 0 citations
This study explores the effects of business intelligence capabilities on AI-driven logistics innovation by conceptualizing two serial mediation mechanisms: decision support and supply chain visibility. Based on the dynamic capability's perspective and resource-based view, the study has the following argument: BI capabilities will enable logistics firms to gather, integrate, analyze, and convert the data from operations to useful knowledge, which helps in making intelligent decisions. A quantitative, explanatory and cross-sectional research design was used. A structured questionnaire was administered to 327 employees/managers in logistics-related companies to gather data. The data was statistically analyzed by SmartPLS software using structural equation modeling. The results indicated that the business intelligence capabilities have a positive impact on decision support as well as AI-powered logistics innovation. Results show that decision support has a positive influence on supply chain visibility, and that supply chain visibility has a positive influence on AI-enabled logistics innovation. Further results demonstrated that BI capabilities and AI-enabled logistics innovation is serially mediated by decision support and supply chain visibility. The research enlightens the literature by detailing how data-driven functionalities can be turned into AI-powered logistics innovation through enhanced decision-making capabilities and supply chain transparency.
Amro Alzghoul, Amineh A. Khaddam, H. Irtaimeh et al.· International Review of Mana...· 0 citations
Artificial Intelligence (AI) has emerged as a transformative enabler for supply chain optimization, offering advanced capabilities in predictive analytics, real-time decision-making, and operational efficiency. However, successful AI integration in supply chain management (SCM) is influenced by multiple interdependent technological, organizational, and governance-related factors. This study aims to identify and structure the key factors affecting AI-driven supply chain optimization in manufacturing industries. A mixed-methods approach was employed. In the qualitative phase, semi-structured interviews with domain experts were conducted to identify critical factors in AI adoption. These factors were then analyzed using Interpretive Structural Modeling (ISM) to develop a hierarchical model of relationships. Subsequently, MICMAC analysis was applied to classify factors based on their driving and dependence power. The results indicate that the technology deployment strategy, governance, compliance, and financial considerations are the most influential independent factors, serving as foundational drivers of AI adoption. Data and system requirements emerged as linkage factors with high driving and dependence power, while operational applications such as demand forecasting and automation were identified as dependent factors. The findings highlight the importance of structured AI strategies and robust data governance frameworks in achieving effective AI-driven supply chain transformation, providing valuable insights for practitioners and policymakers.
Amir Jalilvand-Nejad· International journal of eng...· 0 citations
The integration of artificial intelligence (AI) into project management is reshaping organisational practices by enhancing decision-making, efficiency, and project outcomes. This study examines the relationship between human-AI collaboration and project management performance in Nigeria, with organisational readiness as a moderating variable. A quantitative, cross-sectional survey design was adopted, and data were collected from 120 project managers and team members across construction,information technology, banking, and telecommunications sectors. Data were analysed using descriptive statistics, correlation, regression, and moderation analysis. The findings reveal that human - AI collaboration has a significant positive effect on project management performance, particularly in improving cost efficiency, schedule adherence, and quality delivery. Additionally, organisational readiness significantly moderates this relationship, indicating that organisations with strong digital infrastructure, skilled personnel, and supportive leadership derive greater benefits from AI integration. These results reinforce the relevance of Socio-Technical Systems Theory and the Technology Acceptance Model in explaining AI-enabled project environments.
Mehifu Egogwe Jaiyeola, Sunday Ade Bello· International Journal of Adv...· 0 citations